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In this work human action recognition problem was discussed in video sequences. Solution of the problem was studied in three stages. Firstly, points of interest were detected with preproccesing and these points which are called cuboids were declared in small windows, then feature extraction was performed and finally, human action is decided by using classification. Features extraction is not only...
For resolving the problem of rotation-invariant human detection in natural scene, a rotation-invariant detection algorithm based on polar-HOGs and double-scale direction estimation is proposed in this paper. The algorithm first transforms rotation of object to cycle translation by using polar coordinate mapping, then eliminates the effect of rotation by applying reverse cycle translation to p-θ mapping...
In this paper, we present a complex approach to improve microaneurysm detection in color fundus images. Microaneurysms are early signs of diabetic retinopathy, so it is essential to detect these lesions accurately in an automatic screening system. The recommended detection of microaneurysms is realized through several levels. First, a specific combination of different preprocessing methods for candidate...
We investigate a fast pedestrian localization framework that integrates the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features on a data parallel architecture. The salient features of humans are captured by HoG blocks of variable sizes and locations which are chosen by the AdaBoost algorithm from a large set of possible blocks. We use the integral image representation...
In this paper, we propose a new framework in pedestrian detection using a two-step classification algorithm, which is a ??coarse to fine?? course. The framework consists of a full-body detection (FBD) step and a head-shoulder detection (HSD) step. The FBD step uses fusion of Haar-like and HOG features to get better performance, and the HSD step utilizes edgelet features for classification and detection...
This paper describes a novel technique for detecting a human body direction using SVM constructed by HOG feature selected by AdaBoost. HOG feature is well-known feature for the robust judgment of a human. We employ the feature for detecting a human body direction. We compared some feature selecting methods with the previous one. Experimental results show effectiveness of the proposed method.
This paper presents a novel approach for face detection, which is based on the discriminative MspLBP features selected by a boosting technique called the Ada-LDA method. By scanning the face image with a scalable sub-window, many sub-regions are obtained from which the MspLBP features are extracted to describe the local structures of a face image. From a large pool of the MspLBP features within the...
In this paper we discuss the issue of classifiers combined with histogram of oriented gradients (HOG) descriptors for human detection. And we present a method that combines AdaBoost learning with HOG descriptors. The weak learners used in our algorithm are based on weighted modified quadratic discriminant functions (MQDF) which is a parametric model. We evaluate our algorithm on the INRIA person dataset...
There has been a growth in demand for surveillance equipment to monitor people in indoor as well as outdoor environments. Furthermore, using guards to watch surveillance screens all the time is highly inefficient and thus automation of human monitoring can be more accurate and produce cost savings. The problem is challenging if we choose to use a passive non-invasive sensor such as vision. The specific...
We propose a method that can detect humans in a single image based on a novel cascaded structure. In our approach, both intensity-based rectangle features and gradient-based 1-D features are employed in the feature pool for weak-learner selection. The Real AdaBoost algorithm is used to select critical features from a combined feature set and learn the classifiers from the training images for each...
In this paper, we employ the recent on-line boosting framework to fuse heterogeneous features for object detection and tracking in a video surveillance application. Detection and tracking are treated as a classification problem by an ensemble of weak classifiers built on heterogeneous feature types and updated on-line. We extend the on-line boosting framework by proposing an algorithm that builds...
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